Merge pull request #957 from PyPSA/land-transport-fix
Land transport fix
This commit is contained in:
Fabian Neumann
GitHub
commit
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2
.gitignore
vendored
2
.gitignore
vendored
@ -2,6 +2,8 @@
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#
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# SPDX-License-Identifier: CC0-1.0
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master
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.snakemake*
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.ipynb_checkpoints
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__pycache__
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@ -431,7 +431,6 @@ sector:
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bev_availability: 0.5
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bev_energy: 0.05
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bev_charge_efficiency: 0.9
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bev_plug_to_wheel_efficiency: 0.2
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bev_charge_rate: 0.011
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bev_avail_max: 0.95
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bev_avail_mean: 0.8
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@ -460,8 +459,9 @@ sector:
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2040: 0.3
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2045: 0.15
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2050: 0
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transport_fuel_cell_efficiency: 0.5
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transport_internal_combustion_efficiency: 0.3
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transport_electric_efficiency: 53.19 # 1 MWh_el = 53.19*100 km
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transport_fuel_cell_efficiency: 30.003 # 1 MWh_H2 = 30.003*100 km
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transport_ice_efficiency: 16.0712 # 1 MWh_oil = 16.0712 * 100 km
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agriculture_machinery_electric_share: 0
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agriculture_machinery_oil_share: 1
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agriculture_machinery_fuel_efficiency: 0.7
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@ -1041,6 +1041,7 @@ plotting:
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BEV charger: '#baf238'
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V2G: '#e5ffa8'
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land transport EV: '#baf238'
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land transport demand: '#38baf2'
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Li ion: '#baf238'
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# hot water storage
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water tanks: '#e69487'
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@ -24,7 +24,6 @@ bev_dsm,--,"{true, false}",Add the option for battery electric vehicles (BEV) to
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bev_availability,--,float,The share for battery electric vehicles (BEV) that are able to do demand side management (DSM)
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bev_energy,--,float,The average size of battery electric vehicles (BEV) in MWh
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bev_charge_efficiency,--,float,Battery electric vehicles (BEV) charge and discharge efficiency
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bev_plug_to_wheel _efficiency,km/kWh,float,The distance battery electric vehicles (BEV) can travel in km per kWh of energy charge in battery. Base value comes from `Tesla Model S <https://www.fueleconomy.gov/feg/>`_
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bev_charge_rate,MWh,float,The power consumption for one electric vehicle (EV) in MWh. Value derived from 3-phase charger with 11 kW.
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bev_avail_max,--,float,The maximum share plugged-in availability for passenger electric vehicles.
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bev_avail_mean,--,float,The average share plugged-in availability for passenger electric vehicles.
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@ -32,8 +31,9 @@ v2g,--,"{true, false}",Allows feed-in to grid from EV battery
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land_transport_fuel_cell _share,--,Dictionary with planning horizons as keys.,The share of vehicles that uses fuel cells in a given year
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land_transport_electric _share,--,Dictionary with planning horizons as keys.,The share of vehicles that uses electric vehicles (EV) in a given year
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land_transport_ice _share,--,Dictionary with planning horizons as keys.,The share of vehicles that uses internal combustion engines (ICE) in a given year. What is not EV or FCEV is oil-fuelled ICE.
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transport_fuel_cell _efficiency,--,float,The H2 conversion efficiencies of fuel cells in transport
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transport_internal _combustion_efficiency,--,float,The oil conversion efficiencies of internal combustion engine (ICE) in transport
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transport_electric_efficiency,MWh/100km,float,The conversion efficiencies of electric vehicles in transport
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transport_fuel_cell_efficiency,MWh/100km,float,The H2 conversion efficiencies of fuel cells in transport
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transport_ice_efficiency,MWh/100km,float,The oil conversion efficiencies of internal combustion engine (ICE) in transport
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agriculture_machinery _electric_share,--,float,The share for agricultural machinery that uses electricity
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agriculture_machinery _oil_share,--,float,The share for agricultural machinery that uses oil
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agriculture_machinery _fuel_efficiency,--,float,The efficiency of electric-powered machinery in the conversion of electricity to meet agricultural needs.
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@ -81,6 +81,9 @@ Upcoming Release
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* bugfix: convert Strings to pathlib.Path objects as input to ConfigSettings
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* bugfix: fix distinction of temperature-dependent correction factors for the
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energy demand of electric vehicles, ICES fuel cell cars.
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* Allow the use of more solvers in clustering (Xpress, COPT, Gurobi, CPLEX, SCIP, MOSEK).
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* Enhanced support for choosing different weather years
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@ -396,13 +396,12 @@ def build_idees(countries):
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names=["country", "year"],
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)
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# efficiency kgoe/100km -> ktoe/100km
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totals.loc[:, "passenger car efficiency"] *= 1e3
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# convert ktoe to TWh
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exclude = totals.columns.str.fullmatch("passenger cars")
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totals.loc[:, ~exclude] *= 11.63 / 1e3
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# convert TWh/100km to kWh/km
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totals.loc[:, "passenger car efficiency"] *= 10
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return totals
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@ -24,14 +24,17 @@ logger = logging.getLogger(__name__)
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def build_nodal_transport_data(fn, pop_layout, year):
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# get numbers of car and fuel efficiency per country
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transport_data = pd.read_csv(fn, index_col=[0, 1])
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transport_data = transport_data.xs(min(2015, year), level="year")
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# break number of cars down to nodal level based on population density
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nodal_transport_data = transport_data.loc[pop_layout.ct].fillna(0.0)
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nodal_transport_data.index = pop_layout.index
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nodal_transport_data["number cars"] = (
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pop_layout["fraction"] * nodal_transport_data["number cars"]
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)
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# fill missing fuel efficiency with average data
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nodal_transport_data.loc[
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nodal_transport_data["average fuel efficiency"] == 0.0,
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"average fuel efficiency",
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@ -41,10 +44,13 @@ def build_nodal_transport_data(fn, pop_layout, year):
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def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
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## Get overall demand curve for all vehicles
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"""
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Returns transport demand per bus in unit km driven [100 km].
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"""
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# averaged weekly counts from the year 2010-2015
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traffic = pd.read_csv(traffic_fn, skiprows=2, usecols=["count"]).squeeze("columns")
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# create annual profile take account time zone + summer time
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transport_shape = generate_periodic_profiles(
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dt_index=snapshots,
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nodes=nodes,
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@ -52,15 +58,6 @@ def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
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)
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transport_shape = transport_shape / transport_shape.sum()
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# electric motors are more efficient, so alter transport demand
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plug_to_wheels_eta = options["bev_plug_to_wheel_efficiency"]
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battery_to_wheels_eta = plug_to_wheels_eta * options["bev_charge_efficiency"]
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efficiency_gain = (
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nodal_transport_data["average fuel efficiency"] / battery_to_wheels_eta
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)
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# get heating demand for correction to demand time series
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temperature = xr.open_dataarray(airtemp_fn).to_pandas()
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@ -73,16 +70,7 @@ def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
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options["ICE_upper_degree_factor"],
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)
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dd_EV = transport_degree_factor(
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temperature,
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options["transport_heating_deadband_lower"],
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options["transport_heating_deadband_upper"],
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options["EV_lower_degree_factor"],
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options["EV_upper_degree_factor"],
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)
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# divide out the heating/cooling demand from ICE totals
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# and multiply back in the heating/cooling demand for EVs
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ice_correction = (transport_shape * (1 + dd_ICE)).sum() / transport_shape.sum()
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energy_totals_transport = (
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@ -91,10 +79,11 @@ def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
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- pop_weighted_energy_totals["electricity rail"]
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)
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return (
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(transport_shape.multiply(energy_totals_transport) * 1e6 * nyears)
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.divide(efficiency_gain * ice_correction)
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.multiply(1 + dd_EV)
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# convert average fuel efficiency from kW/100 km -> MW/100km
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eff = nodal_transport_data["average fuel efficiency"] / 1e3
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return (transport_shape.multiply(energy_totals_transport) * 1e6 * nyears).divide(
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eff * ice_correction
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)
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@ -131,11 +120,14 @@ def bev_availability_profile(fn, snapshots, nodes, options):
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"""
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Derive plugged-in availability for passenger electric vehicles.
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"""
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# car count in typical week
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traffic = pd.read_csv(fn, skiprows=2, usecols=["count"]).squeeze("columns")
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# maximum share plugged-in availability for passenger electric vehicles
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avail_max = options["bev_avail_max"]
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# average share plugged-in availability for passenger electric vehicles
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avail_mean = options["bev_avail_mean"]
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# linear scaling, highest when traffic is lowest, decreases if traffic increases
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avail = avail_max - (avail_max - avail_mean) * (traffic - traffic.min()) / (
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traffic.mean() - traffic.min()
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)
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@ -156,6 +148,8 @@ def bev_availability_profile(fn, snapshots, nodes, options):
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def bev_dsm_profile(snapshots, nodes, options):
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dsm_week = np.zeros((24 * 7,))
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# assuming that at a certain time ("bev_dsm_restriction_time") EVs have to
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# be charged to a minimum value (defined in bev_dsm_restriction_value)
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dsm_week[(np.arange(0, 7, 1) * 24 + options["bev_dsm_restriction_time"])] = options[
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"bev_dsm_restriction_value"
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]
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@ -167,6 +161,7 @@ def bev_dsm_profile(snapshots, nodes, options):
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)
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# %%
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if __name__ == "__main__":
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if "snakemake" not in globals():
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from _helpers import mock_snakemake
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@ -174,7 +169,7 @@ if __name__ == "__main__":
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snakemake = mock_snakemake(
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"build_transport_demand",
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simpl="",
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clusters=60,
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clusters=37,
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)
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configure_logging(snakemake)
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set_scenario_config(snakemake)
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@ -29,6 +29,7 @@ from build_energy_totals import (
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build_eurostat,
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build_eurostat_co2,
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)
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from build_transport_demand import transport_degree_factor
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from networkx.algorithms import complement
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from networkx.algorithms.connectivity.edge_augmentation import k_edge_augmentation
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from prepare_network import maybe_adjust_costs_and_potentials
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@ -1501,103 +1502,122 @@ def add_storage_and_grids(n, costs):
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)
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def add_land_transport(n, costs):
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# TODO options?
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logger.info("Add land transport")
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transport = pd.read_csv(
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snakemake.input.transport_demand, index_col=0, parse_dates=True
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)
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number_cars = pd.read_csv(snakemake.input.transport_data, index_col=0)[
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"number cars"
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]
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avail_profile = pd.read_csv(
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snakemake.input.avail_profile, index_col=0, parse_dates=True
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)
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dsm_profile = pd.read_csv(
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snakemake.input.dsm_profile, index_col=0, parse_dates=True
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)
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fuel_cell_share = get(options["land_transport_fuel_cell_share"], investment_year)
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electric_share = get(options["land_transport_electric_share"], investment_year)
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ice_share = get(options["land_transport_ice_share"], investment_year)
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total_share = fuel_cell_share + electric_share + ice_share
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def check_land_transport_shares(shares):
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# Sums up the shares, ignoring None values
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total_share = sum(filter(None, shares))
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if total_share != 1:
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logger.warning(
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f"Total land transport shares sum up to {total_share:.2%}, corresponding to increased or decreased demand assumptions."
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f"Total land transport shares sum up to {total_share:.2%},"
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"corresponding to increased or decreased demand assumptions."
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)
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logger.info(f"FCEV share: {fuel_cell_share*100}%")
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logger.info(f"EV share: {electric_share*100}%")
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logger.info(f"ICEV share: {ice_share*100}%")
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nodes = pop_layout.index
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def get_temp_efficency(
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car_efficiency,
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temperature,
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deadband_lw,
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deadband_up,
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degree_factor_lw,
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degree_factor_up,
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):
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"""
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Correct temperature depending on heating and cooling for respective car
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type.
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"""
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# temperature correction for EVs
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dd = transport_degree_factor(
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temperature,
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deadband_lw,
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deadband_up,
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degree_factor_lw,
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degree_factor_up,
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)
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temp_eff = 1 / (1 + dd)
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return car_efficiency * temp_eff
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def add_EVs(
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n,
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avail_profile,
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dsm_profile,
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p_set,
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electric_share,
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number_cars,
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temperature,
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):
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if electric_share > 0:
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n.add("Carrier", "Li ion")
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n.madd(
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"Bus",
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nodes,
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spatial.nodes,
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suffix=" EV battery",
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location=nodes,
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location=spatial.nodes,
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carrier="Li ion",
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unit="MWh_el",
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)
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p_set = (
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electric_share
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* (
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transport[nodes]
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+ cycling_shift(transport[nodes], 1)
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+ cycling_shift(transport[nodes], 2)
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)
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/ 3
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car_efficiency = options["transport_electric_efficiency"]
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# temperature corrected efficiency
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efficiency = get_temp_efficency(
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car_efficiency,
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temperature,
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options["transport_heating_deadband_lower"],
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options["transport_heating_deadband_upper"],
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options["EV_lower_degree_factor"],
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options["EV_upper_degree_factor"],
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)
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p_shifted = (p_set + cycling_shift(p_set, 1) + cycling_shift(p_set, 2)) / 3
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cyclic_eff = p_set.div(p_shifted)
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efficiency *= cyclic_eff
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profile = electric_share * p_set.div(efficiency)
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n.madd(
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"Load",
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nodes,
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spatial.nodes,
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suffix=" land transport EV",
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bus=nodes + " EV battery",
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bus=spatial.nodes + " EV battery",
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carrier="land transport EV",
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p_set=p_set,
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p_set=profile,
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)
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p_nom = number_cars * options.get("bev_charge_rate", 0.011) * electric_share
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n.madd(
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"Link",
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nodes,
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spatial.nodes,
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suffix=" BEV charger",
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bus0=nodes,
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bus1=nodes + " EV battery",
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bus0=spatial.nodes,
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bus1=spatial.nodes + " EV battery",
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p_nom=p_nom,
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carrier="BEV charger",
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p_max_pu=avail_profile[nodes],
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p_max_pu=avail_profile[spatial.nodes],
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lifetime=1,
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efficiency=options.get("bev_charge_efficiency", 0.9),
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# These were set non-zero to find LU infeasibility when availability = 0.25
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# p_nom_extendable=True,
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# p_nom_min=p_nom,
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# capital_cost=1e6, #i.e. so high it only gets built where necessary
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)
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if electric_share > 0 and options["v2g"]:
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if options["v2g"]:
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n.madd(
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"Link",
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nodes,
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spatial.nodes,
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suffix=" V2G",
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bus1=nodes,
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bus0=nodes + " EV battery",
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bus1=spatial.nodes,
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bus0=spatial.nodes + " EV battery",
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p_nom=p_nom,
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carrier="V2G",
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p_max_pu=avail_profile[nodes],
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p_max_pu=avail_profile[spatial.nodes],
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lifetime=1,
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efficiency=options.get("bev_charge_efficiency", 0.9),
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)
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if electric_share > 0 and options["bev_dsm"]:
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if options["bev_dsm"]:
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e_nom = (
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number_cars
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* options.get("bev_energy", 0.05)
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@ -1607,43 +1627,65 @@ def add_land_transport(n, costs):
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n.madd(
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"Store",
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nodes,
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spatial.nodes,
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suffix=" battery storage",
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bus=nodes + " EV battery",
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bus=spatial.nodes + " EV battery",
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carrier="battery storage",
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e_cyclic=True,
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e_nom=e_nom,
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e_max_pu=1,
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e_min_pu=dsm_profile[nodes],
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e_min_pu=dsm_profile[spatial.nodes],
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)
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if fuel_cell_share > 0:
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def add_fuel_cell_cars(n, p_set, fuel_cell_share, temperature):
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car_efficiency = options["transport_fuel_cell_efficiency"]
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# temperature corrected efficiency
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efficiency = get_temp_efficency(
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car_efficiency,
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temperature,
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options["transport_heating_deadband_lower"],
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options["transport_heating_deadband_upper"],
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options["ICE_lower_degree_factor"],
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options["ICE_upper_degree_factor"],
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)
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profile = fuel_cell_share * p_set.div(efficiency)
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n.madd(
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"Load",
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nodes,
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spatial.nodes,
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suffix=" land transport fuel cell",
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bus=nodes + " H2",
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bus=spatial.h2.nodes,
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carrier="land transport fuel cell",
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p_set=fuel_cell_share
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/ options["transport_fuel_cell_efficiency"]
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* transport[nodes],
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p_set=profile,
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)
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|
||||
if ice_share > 0:
|
||||
|
||||
def add_ice_cars(n, p_set, ice_share, temperature):
|
||||
|
||||
add_carrier_buses(n, "oil")
|
||||
|
||||
ice_efficiency = options["transport_internal_combustion_efficiency"]
|
||||
car_efficiency = options["transport_ice_efficiency"]
|
||||
|
||||
p_set_land_transport_oil = (
|
||||
ice_share
|
||||
/ ice_efficiency
|
||||
* transport[nodes].rename(columns=lambda x: x + " land transport oil")
|
||||
# temperature corrected efficiency
|
||||
efficiency = get_temp_efficency(
|
||||
car_efficiency,
|
||||
temperature,
|
||||
options["transport_heating_deadband_lower"],
|
||||
options["transport_heating_deadband_upper"],
|
||||
options["ICE_lower_degree_factor"],
|
||||
options["ICE_upper_degree_factor"],
|
||||
)
|
||||
|
||||
profile = ice_share * p_set.div(efficiency).rename(
|
||||
columns=lambda x: x + " land transport oil"
|
||||
)
|
||||
|
||||
if not options["regional_oil_demand"]:
|
||||
p_set_land_transport_oil = p_set_land_transport_oil.sum(axis=1).to_frame(
|
||||
name="EU land transport oil"
|
||||
)
|
||||
profile = profile.sum(axis=1).to_frame(name="EU land transport oil")
|
||||
|
||||
n.madd(
|
||||
"Bus",
|
||||
@ -1658,7 +1700,7 @@ def add_land_transport(n, costs):
|
||||
spatial.oil.land_transport,
|
||||
bus=spatial.oil.land_transport,
|
||||
carrier="land transport oil",
|
||||
p_set=p_set_land_transport_oil,
|
||||
p_set=profile,
|
||||
)
|
||||
|
||||
n.madd(
|
||||
@ -1673,6 +1715,56 @@ def add_land_transport(n, costs):
|
||||
)
|
||||
|
||||
|
||||
def add_land_transport(n, costs):
|
||||
|
||||
logger.info("Add land transport")
|
||||
|
||||
# read in transport demand in units driven km [100 km]
|
||||
transport = pd.read_csv(
|
||||
snakemake.input.transport_demand, index_col=0, parse_dates=True
|
||||
)
|
||||
number_cars = pd.read_csv(snakemake.input.transport_data, index_col=0)[
|
||||
"number cars"
|
||||
]
|
||||
avail_profile = pd.read_csv(
|
||||
snakemake.input.avail_profile, index_col=0, parse_dates=True
|
||||
)
|
||||
dsm_profile = pd.read_csv(
|
||||
snakemake.input.dsm_profile, index_col=0, parse_dates=True
|
||||
)
|
||||
|
||||
# exogenous share of passenger car type
|
||||
engine_types = ["fuel_cell", "electric", "ice"]
|
||||
shares = pd.Series()
|
||||
for engine in engine_types:
|
||||
shares[engine] = get(options[f"land_transport_{engine}_share"], investment_year)
|
||||
logger.info(f"{engine} share: {shares[engine]*100}%")
|
||||
|
||||
check_land_transport_shares(shares)
|
||||
|
||||
p_set = transport[spatial.nodes]
|
||||
|
||||
# temperature for correction factor for heating/cooling
|
||||
temperature = xr.open_dataarray(snakemake.input.temp_air_total).to_pandas()
|
||||
|
||||
if shares["electric"] > 0:
|
||||
add_EVs(
|
||||
n,
|
||||
avail_profile,
|
||||
dsm_profile,
|
||||
p_set,
|
||||
shares["electric"],
|
||||
number_cars,
|
||||
temperature,
|
||||
)
|
||||
|
||||
if shares["fuel_cell"] > 0:
|
||||
add_fuel_cell_cars(n, p_set, shares["fuel_cell"], temperature)
|
||||
|
||||
if shares["ice"] > 0:
|
||||
add_ice_cars(n, p_set, shares["ice"], temperature)
|
||||
|
||||
|
||||
def build_heat_demand(n):
|
||||
heat_demand_shape = (
|
||||
xr.open_dataset(snakemake.input.hourly_heat_demand_total)
|
||||
@ -3666,19 +3758,20 @@ def lossy_bidirectional_links(n, carrier, efficiencies={}):
|
||||
)
|
||||
|
||||
|
||||
# %%
|
||||
if __name__ == "__main__":
|
||||
if "snakemake" not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
|
||||
snakemake = mock_snakemake(
|
||||
"prepare_sector_network",
|
||||
configfiles="test/config.overnight.yaml",
|
||||
# configfiles="test/config.overnight.yaml",
|
||||
simpl="",
|
||||
opts="",
|
||||
clusters="37",
|
||||
ll="v1.0",
|
||||
sector_opts="CO2L0-24h-T-H-B-I-A-dist1",
|
||||
planning_horizons="2030",
|
||||
sector_opts="730H-T-H-B-I-A-dist1",
|
||||
planning_horizons="2050",
|
||||
)
|
||||
|
||||
configure_logging(snakemake)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user